Plate-YOLO: An End-to-End Object Detection Framework for Robust Astrometric Registration of Historical Plates

Historical astronomical plates provide a long temporal baseline for time-domain astrophysics, but degradation and structural artifacts can hinder automated astrometric calibration. We present Plate-YOLO, a framework that combines source detection and stellar classification in one detector and supplies source catalogs to a separate astrometric solver. The detector adapts YOLOv5 with Space-to-Depth feature rearrangement, SimAM attention, a high-resolution P2 head, and a Normalized Wasserstein Distance loss for small bounding boxes. A Gaia-guided fine-tuning procedure uses initially solved plates to construct labels for a new archive. On 150 UBAI plates for which the conventional pipeline had failed, the Chinese-trained model solved 40 plates, and target-archive fine-tuning recovered a further 106, giving an overall recovery rate of 146/150 (97.3%). In the reported hardware configuration, source extraction took 6.61 s per plate for Plate-YOLO and 16.73 s for SExtractor. These results demonstrate the practical value of adapting source detection to degraded historical plates.